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This is patch to fix a bug affecting PoolPrev. The bug
affected the maximum likelihood estimates (MLE) and likelihood ratio
confidence intervals (LR-CIs) of prevalence when the default Jeffrey’s
prior was being used. The bug would usually make the MLE and LR-CIs much
closer to the Bayesian estimates than they should have been. As both
sets of estimates are valid, the results will still have been
approximately correct.
This patch also includes an option, replicate.poolscreen
(default to FALSE), for PoolPrev. This options
changes the way the likelihood ratio confidence intervals are
calculated. With replicate.poolscreen = TRUE PoolPrev will
more closely reproduce the results produced by Poolscreen. We believe
that our implementation of these intervals is more correct so would
recommend that users continue to use the default
(replicate.poolscreen = FALSE), but this option may be
helpful for those who are trying to compare results across the two
programs.
We have published a paper about PoolTestR in Environmental Modelling and Software now available at https://doi.org/10.1016/j.envsoft.2021.105158. If you find this package useful, please let us know and/or cite our paper!
A couple bug fixes: * corrections to the Jeffrey’s prior in PoolPrev * improved numerical stability of hierarchical models – previous implementation was causing initialisation of MCMC to fail in some edge cases
A few improvements: * Allow user to specify level of confidence intervals and credible intervals * option for getPrevalence to return the posterior median as the point estimate (instead of the posterior mean) for Bayesian models with with PoolRegBayes * Implement PoolRegBayes with a logit link function as custom family in brms. This allows for better post-processing for the results of PoolRegBayes – e.g. simulating from the model, leave-one-out cross-validation, posterior predictive checks. see brms for details * Allow users to pass more control variables to MCMC sampling routines across PoolRegBayes, HierPoolPrev, and PoolPrev * Allows users to specify the scale parameter for the half-Cauchy hyper-prior or the standard deviations of the random effect terms in HierPoolPrev. Also reduced the default value of this hyperprior from 25 (very diffuse) to 2 (weakly informative). This is now very comparable to the equivalent default hyper-prior for brms models including those fit using PoolRegBayes (i.e. a half t distribution three degrees of freedom )
Minor patch so that the package works across more platforms (namely solaris)
This is our first official release! Please see the github site (https://github.com/AngusMcLure/PoolTestR#pooltestr) for a basic crash course on using the package. An upcoming (open access) journal article will go into further detail. A preprint can be accessed at https://arxiv.org/abs/2012.05405. I’ll post a link to the article when published.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.